





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel





class 					A__       :




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase=13   ,    lowercase=32   ,    lowercase=2   ,    lowercase=3   ,    lowercase=16   ,    lowercase=[1, 2, 1]   ,    lowercase=[2, 2, 4]   ,    lowercase=2   ,    lowercase=2.0   ,    lowercase=True   ,    lowercase=0.0   ,    lowercase=0.0   ,    lowercase=0.1   ,    lowercase="gelu"   ,    lowercase=False   ,    lowercase=True   ,    lowercase=0.02   ,    lowercase=1e-5   ,    lowercase=True   ,    lowercase=None   ,    lowercase=True   ,    lowercase=10   ,    lowercase=8   ,    lowercase=["stage1", "stage2", "stage3"]   ,    lowercase=[1, 2, 3]   ,    )     ->		List[Any]:







          '''simple docstring'''

          a__		:      Any					=							parent
          a__		:      str					=							batch_size
          a__		:      Optional[int]					=							image_size
          a__		:      Union[str, Any]					=							patch_size
          a__		:      List[str]					=							num_channels
          a__		:      Optional[Any]					=							embed_dim
          a__		:      int					=							depths
          a__		:      Optional[Any]					=							num_heads
          a__		:      List[str]					=							window_size
          a__		:      Tuple					=							mlp_ratio
          a__		:      Union[str, Any]					=							qkv_bias
          a__		:      Optional[int]					=							hidden_dropout_prob
          a__		:      Tuple					=							attention_probs_dropout_prob
          a__		:      int					=							drop_path_rate
          a__		:      Union[str, Any]					=							hidden_act
          a__		:      Union[str, Any]					=							use_absolute_embeddings
          a__		:      Union[str, Any]					=							patch_norm
          a__		:      Any					=							layer_norm_eps
          a__		:      Optional[Any]					=							initializer_range
          a__		:      Dict					=							is_training
          a__		:      Tuple					=							scope
          a__		:      Optional[int]					=							use_labels
          a__		:      Union[str, Any]					=							type_sequence_label_size
          a__		:      Dict					=							encoder_stride
          a__		:      Union[str, Any]					=							out_features
          a__		:      Union[str, Any]					=							out_indices

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__		:      Optional[Any]					=							None
          if self.use_labels:
                 a__		:      Any					=							ids_tensor([self.batch_size]   ,    self.type_sequence_label_size)

          a__		:      Dict					=							self.get_config()

          return config, pixel_values, labels

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          return MaskFormerSwinConfig(
              image_size=self.image_size   ,    patch_size=self.patch_size   ,    num_channels=self.num_channels   ,    embed_dim=self.embed_dim   ,    depths=self.depths   ,    num_heads=self.num_heads   ,    window_size=self.window_size   ,    mlp_ratio=self.mlp_ratio   ,    qkv_bias=self.qkv_bias   ,    hidden_dropout_prob=self.hidden_dropout_prob   ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,    drop_path_rate=self.drop_path_rate   ,    hidden_act=self.hidden_act   ,    use_absolute_embeddings=self.use_absolute_embeddings   ,    path_norm=self.patch_norm   ,    layer_norm_eps=self.layer_norm_eps   ,    initializer_range=self.initializer_range   ,    encoder_stride=self.encoder_stride   ,    out_features=self.out_features   ,    out_indices=self.out_indices   ,    )

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							MaskFormerSwinModel(config=UpperCamelCase__)
          model.to(UpperCamelCase__)
          model.eval()
          a__		:      int					=							model(UpperCamelCase__)

          a__		:      List[Any]					=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
          a__		:      int					=							int(config.embed_dim * 2 ** (len(config.depths) - 1))

          self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, expected_seq_len, expected_dim))

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Any					=							MaskFormerSwinBackbone(config=UpperCamelCase__)
          model.to(UpperCamelCase__)
          model.eval()
          a__		:      str					=							model(UpperCamelCase__)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)   ,    len(config.out_features))
          self.parent.assertListEqual(list(result.feature_maps[0].shape)   ,    [13, 16, 16, 16])

          # verify channels
          self.parent.assertEqual(len(model.channels)   ,    len(config.out_features))
          self.parent.assertListEqual(model.channels   ,    [16, 32, 64])

          # verify ValueError
          with self.parent.assertRaises(UpperCamelCase__):
                 a__		:      List[Any]					=							['stem']
                 a__		:      str					=							MaskFormerSwinBackbone(config=UpperCamelCase__)

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Optional[int]					=							self.prepare_config_and_inputs()
          a__					,				a__					,				a__		:      str					=							config_and_inputs
          a__		:      str					=							{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class 					A__       (				__A					,    __A					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							Optional[Any]			    =    (
       (
           MaskFormerSwinModel,
           MaskFormerSwinBackbone,
       )
       if is_torch_available()
       else ()
   )
   __A  :							List[str]			    =    {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
   __A  :							List[str]			    =    False
   __A  :							Tuple			    =    False
   __A  :							Tuple			    =    False
   __A  :							List[Any]			    =    False
   __A  :							Optional[int]			    =    False

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          a__		:      Optional[int]					=							MaskFormerSwinModelTester(self)
          a__		:      int					=							ConfigTester(self   ,    config_class=UpperCamelCase__   ,    embed_dim=37)

   @require_torch_multi_gpu
   @unittest.skip(
       reason=(
           '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
           ' `nn.DataParallel`'
       ))
   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()

   def 				__lowercase						(   self)     ->		List[str]:







          '''simple docstring'''

          return

   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase__)

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      Any					=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*UpperCamelCase__)

   @unittest.skip('Swin does not use inputs_embeds')
   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          pass

   @unittest.skip('Swin does not support feedforward chunking')
   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__					,				a__		:      Optional[int]					=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__		:      Union[str, Any]					=							model_class(UpperCamelCase__)
                 self.assertIsInstance(model.get_input_embeddings()   ,    (nn.Module))
                 a__		:      Any					=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(UpperCamelCase__   ,    nn.Linear))

   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__					,				a__		:      int					=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__		:      str					=							model_class(UpperCamelCase__)
                 a__		:      str					=							inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__		:      List[Any]					=							[*signature.parameters.keys()]

                 a__		:      Union[str, Any]					=							['pixel_values']
                 self.assertListEqual(arg_names[:1]   ,    UpperCamelCase__)

   @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions')
   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          pass

   @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone')
   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase   ,    lowercase   ,    lowercase)     ->		List[str]:







          '''simple docstring'''

          a__		:      Optional[int]					=							model_class(UpperCamelCase__)
          model.to(UpperCamelCase__)
          model.eval()

          with torch.no_grad():
                 a__		:      Any					=							model(**self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__))

          a__		:      Dict					=							outputs.hidden_states

          a__		:      Any					=							getattr(
              self.model_tester   ,    'expected_num_hidden_layers'   ,    len(self.model_tester.depths) + 1)
          self.assertEqual(len(UpperCamelCase__)   ,    UpperCamelCase__)

          # Swin has a different seq_length
          a__		:      str					=							(
              config.patch_size
              if isinstance(config.patch_size   ,    collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          a__		:      List[str]					=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:])   ,    [num_patches, self.model_tester.embed_dim]   ,    )

   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          a__					,				a__		:      Optional[Any]					=							self.model_tester.prepare_config_and_inputs_for_common()

          a__		:      List[str]					=							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size   ,    collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
                 a__		:      Dict					=							True
                 self.check_hidden_states_output(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__		:      Any					=							True

                 self.check_hidden_states_output(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__)

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__					,				a__		:      Any					=							self.model_tester.prepare_config_and_inputs_for_common()
          a__		:      Dict					=							3

          a__		:      Optional[int]					=							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size   ,    collections.abc.Iterable)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          a__		:      List[Any]					=							(
              config.patch_size
              if isinstance(config.patch_size   ,    collections.abc.Iterable)
              else (config.patch_size, config.patch_size)
          )

          a__		:      Union[str, Any]					=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          a__		:      List[Any]					=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
                 a__		:      List[str]					=							True
                 self.check_hidden_states_output(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    (padded_height, padded_width))

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__		:      Any					=							True
                 self.check_hidden_states_output(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    (padded_height, padded_width))

   @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints')
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          pass

   @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          pass

   @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin')
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          pass

   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__					,				a__		:      Tuple					=							self.model_tester.prepare_config_and_inputs_for_common()

          def set_nan_tensor_to_zero(lowercase):
                 a__		:      Union[str, Any]					=							0
                 return t

          def check_equivalence(lowercase   ,    lowercase   ,    lowercase   ,    lowercase={}):
                 with torch.no_grad():
                        a__		:      List[Any]					=							model(**UpperCamelCase__   ,    return_dict=UpperCamelCase__   ,    **UpperCamelCase__)
                        a__		:      Tuple					=							model(**UpperCamelCase__   ,    return_dict=UpperCamelCase__   ,    **UpperCamelCase__).to_tuple()

                        def recursive_check(lowercase   ,    lowercase):
                               if isinstance(UpperCamelCase__   ,    (List, Tuple)):
                                      for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__   ,    UpperCamelCase__):
                                             recursive_check(UpperCamelCase__   ,    UpperCamelCase__)
                               elif isinstance(UpperCamelCase__   ,    UpperCamelCase__):
                                      for tuple_iterable_value, dict_iterable_value in zip(
                                          tuple_object.values()   ,    dict_object.values()):
                                             recursive_check(UpperCamelCase__   ,    UpperCamelCase__)
                               elif tuple_object is None:
                                      return
                               else:
                                      self.assertTrue(
                                          torch.allclose(
                                              set_nan_tensor_to_zero(UpperCamelCase__)   ,    set_nan_tensor_to_zero(UpperCamelCase__)   ,    atol=1e-5)   ,    msg=(
                                              'Tuple and dict output are not equal. Difference:'
                                              F' {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:'
                                              F' {torch.isnan(UpperCamelCase__).any()} and `inf`: {torch.isinf(UpperCamelCase__)}. Dict has'
                                              F' `nan`: {torch.isnan(UpperCamelCase__).any()} and `inf`: {torch.isinf(UpperCamelCase__)}.'
                                          )   ,    )

                        recursive_check(UpperCamelCase__   ,    UpperCamelCase__)

          for model_class in self.all_model_classes:
                 a__		:      List[str]					=							model_class(UpperCamelCase__)
                 model.to(UpperCamelCase__)
                 model.eval()

                 a__		:      Dict					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__)
                 a__		:      str					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__)
                 check_equivalence(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__)

                 a__		:      int					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__   ,    return_labels=UpperCamelCase__)
                 a__		:      Optional[Any]					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__   ,    return_labels=UpperCamelCase__)
                 check_equivalence(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__)

                 a__		:      Tuple					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__)
                 a__		:      List[Any]					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__)
                 check_equivalence(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    {'output_hidden_states': True})

                 a__		:      Union[str, Any]					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__   ,    return_labels=UpperCamelCase__)
                 a__		:      Union[str, Any]					=							self._prepare_for_class(UpperCamelCase__   ,    UpperCamelCase__   ,    return_labels=UpperCamelCase__)
                 check_equivalence(UpperCamelCase__   ,    UpperCamelCase__   ,    UpperCamelCase__   ,    {'output_hidden_states': True})





@require_torch
class 					A__       (				unittest.TestCase					,    __A      ):




   """simple docstring"""



   __A  :							Any			    =    (MaskFormerSwinBackbone,) if is_torch_available() else ()
   __A  :							List[Any]			    =    MaskFormerSwinConfig

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          a__		:      Dict					=							MaskFormerSwinModelTester(self)

   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          a__					,				a__		:      List[Any]					=							self.model_tester.prepare_config_and_inputs_for_common()
          a__		:      Optional[Any]					=							inputs_dict['pixel_values'].shape[0]

          for backbone_class in self.all_model_classes:
                 a__		:      str					=							backbone_class(UpperCamelCase__)
                 backbone.to(UpperCamelCase__)
                 backbone.eval()

                 a__		:      Optional[Any]					=							backbone(**UpperCamelCase__)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps   ,    UpperCamelCase__)
                 self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
                 for feature_map, n_channels in zip(outputs.feature_maps   ,    backbone.channels):
                        self.assertTrue(feature_map.shape[:2]   ,    (batch_size, n_channels))
                 self.assertIsNone(outputs.hidden_states)
                 self.assertIsNone(outputs.attentions)

                 # Test output_hidden_states=True
                 a__		:      Tuple					=							backbone(**UpperCamelCase__   ,    output_hidden_states=UpperCamelCase__)
                 self.assertIsNotNone(outputs.hidden_states)
                 self.assertTrue(len(outputs.hidden_states)   ,    len(backbone.stage_names))
                 # We skip the stem layer
                 for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,    backbone.channels):
                        for hidden_state in hidden_states:
                               # Hidden states are in the format (batch_size, (height * width), n_channels)
                               a__					,				a__					,				a__		:      Tuple					=							hidden_state.shape
                               self.assertTrue((h_batch_size, h_n_channels)   ,    (batch_size, n_channels))

            # Test output_attentions=True
                 if self.has_attentions:
                        a__		:      Optional[Any]					=							backbone(**UpperCamelCase__   ,    output_attentions=UpperCamelCase__)
                        self.assertIsNotNone(outputs.attentions)










'''simple docstring'''







import argparse
import os
import re


__lowerCAmelCase        =							"src/diffusers"

# Pattern that looks at the indentation in a line.
__lowerCAmelCase        =							re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCAmelCase        =							re.compile(R"\[([^\]]+)\]")


def       __UpperCamelCase					(      lowercase_       :  Any   ):
       """simple docstring"""




       a_						     =			_re_indent.search(lowercase_   )
       return "" if search is None else search.groups()[0]


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  int=""   ,       lowercase_       :  Union[str, Any]=None   ,       lowercase_       :  str=None   ):
       """simple docstring"""




       a_						     =			0
       a_						     =			code.split('\n'   )
       if start_prompt is not None:
              while not lines[index].startswith(lowercase_   ):
                     index += 1
              a_						     =			['\n'.join(lines[:index]   )]
       else:
              a_						     =			[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_						     =			[lines[index]]
       index += 1
       while index < len(lowercase_   ) and (end_prompt is None or not lines[index].startswith(lowercase_   )):
              if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
                     if len(lowercase_   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + ' '   ):
                            current_block.append(lines[index]   )
                            blocks.append('\n'.join(lowercase_   )   )
                            if index < len(lowercase_   ) - 1:
                                   a_						     =			[lines[index + 1]]
                                   index += 1
                            else:
                                   a_						     =			[]
                     else:
                            blocks.append('\n'.join(lowercase_   )   )
                            a_						     =			[lines[index]]
              else:
                     current_block.append(lines[index]   )
              index += 1

       # Adds current block if it's nonempty.
       if len(lowercase_   ) > 0:
              blocks.append('\n'.join(lowercase_   )   )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(lowercase_   ):
              blocks.append('\n'.join(lines[index:]   )   )

       return blocks


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       def _inner(lowercase_       :  Union[str, Any]   ):
              return key(lowercase_   ).lower().replace('_'   ,       ''   )

       return _inner


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int=None   ):
       """simple docstring"""




       def noop(lowercase_       :  str   ):
              return x

       if key is None:
              a_						     =			noop
       # Constants are all uppercase, they go first.
       a_						     =			[obj for obj in objects if key(lowercase_   ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_						     =			[obj for obj in objects if key(lowercase_   )[0].isupper() and not key(lowercase_   ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_						     =			[obj for obj in objects if not key(lowercase_   )[0].isupper()]

       a_						     =			ignore_underscore(lowercase_   )
       return sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       def _replace(lowercase_       :  List[Any]   ):
              a_						     =			match.groups()[0]
              if "," not in imports:
                     return F'[{imports}]'
              a_						     =			[part.strip().replace('"'   ,       ''   ) for part in imports.split(','   )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]   ) == 0:
                     a_						     =			keys[:-1]
              return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_   )]   ) + "]"

       a_						     =			import_statement.split('\n'   )
       if len(lowercase_   ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_						     =			2 if lines[1].strip() == '[' else 1
              a_						     =			[(i, _re_strip_line.search(lowercase_   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
              a_						     =			sort_objects(lowercase_   ,       key=lambda lowercase_   : x[1]   )
              a_						     =			[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
       elif len(lowercase_   ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]   ) is not None:
                     a_						     =			_re_bracket_content.sub(_replace   ,       lines[1]   )
              else:
                     a_						     =			[part.strip().replace('"'   ,       ''   ) for part in lines[1].split(','   )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]   ) == 0:
                            a_						     =			keys[:-1]
                     a_						     =			get_indent(lines[1]   ) + ', '.join([F'"{k}"' for k in sort_objects(lowercase_   )]   )
              return "\n".join(lowercase_   )
       else:
              # Finally we have to deal with imports fitting on one line
              a_						     =			_re_bracket_content.sub(_replace   ,       lowercase_   )
              return import_statement


def       __UpperCamelCase					(      lowercase_       :  Dict   ,       lowercase_       :  List[Any]=True   ):
       """simple docstring"""




       with open(lowercase_   ,       'r'   ) as f:
              a_						     =			f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_						     =			split_code_in_indented_blocks(
           lowercase_   ,       start_prompt='_import_structure = {'   ,       end_prompt='if TYPE_CHECKING:'   )

       # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1   ,       len(lowercase_   ) - 1   ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_						     =			main_blocks[block_idx]
              a_						     =			block.split('\n'   )

              # Get to the start of the imports.
              a_						     =			0
              while line_idx < len(lowercase_   ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_						     =			len(lowercase_   )
                     else:
                            line_idx += 1
              if line_idx >= len(lowercase_   ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_						     =			'\n'.join(block_lines[line_idx:-1]   )
              a_						     =			get_indent(block_lines[1]   )
              # Slit the internal block into blocks of indent level 1.
              a_						     =			split_code_in_indented_blocks(lowercase_   ,       indent_level=lowercase_   )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_						     =			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_						     =			[(pattern.search(lowercase_   ).groups()[0] if pattern.search(lowercase_   ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_						     =			[(i, key) for i, key in enumerate(lowercase_   ) if key is not None]
              a_						     =			[x[0] for x in sorted(lowercase_   ,       key=lambda lowercase_   : x[1]   )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_						     =			0
              a_						     =			[]
              for i in range(len(lowercase_   )   ):
                     if keys[i] is None:
                            reordered_blocks.append(internal_blocks[i]   )
                     else:
                            a_						     =			sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
                            reordered_blocks.append(lowercase_   )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_						     =			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]   )

       if code != "\n".join(lowercase_   ):
              if check_only:
                     return True
              else:
                     print(F'Overwriting {file}.'   )
                     with open(lowercase_   ,       'w'   ) as f:
                            f.write('\n'.join(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int=True   ):
       """simple docstring"""




       a_						     =			[]
       for root, _, files in os.walk(lowercase_   ):
              if "__init__.py" in files:
                     a_						     =			sort_imports(os.path.join(lowercase_   ,       '__init__.py'   )   ,       check_only=lowercase_   )
                     if result:
                            a_						     =			[os.path.join(lowercase_   ,       '__init__.py'   )]
       if len(lowercase_   ) > 0:
              raise ValueError(F'Would overwrite {len(lowercase_   )} files, run `make style`.'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
  __lowerCAmelCase        =							parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)




